What are the features of serving CV models on edge devices?

When deploying computer vision (CV) models on edge devices, it is important to consider resource limitations and real-time operation features. Key features:

• Limited computing resources and memory. Edge devices often have weak CPUs/GPUs, limited RAM, and power, so models must be compact and optimized.
• Model optimization. Techniques such as quantization, pruning, compression, and graph optimization are used to reduce size and speed up inference.
• Low latency. Fast data processing is crucial for real-time applications, so models and pipelines should be as efficient as possible.
• Local data processing. For privacy and traffic reduction, data is processed on the device, requiring autonomy and robustness of the model.
• Hardware acceleration support. Using specialized neural network accelerators (NPU, TPU, DSP) to enhance performance.
• Update and monitoring. Mechanisms for updating models and monitoring their operation on remote devices should be provided.

Example: for an edge device with a camera, a lightweight MobileNet model with INT8 quantization can be used to ensure fast and energy-efficient image processing in real-time.